Systems and methods for determining a likelihood of user termination of services

ABSTRACT

Systems and methods are described herein for generating a model to determine a likelihood that a user will change a service. A media guidance application may identify a first service to which a plurality of users are subscribed, receive information from a third-party data source for each of the plurality of users, identify, based on the respective information from the third-party data source, a user action performed by each of the plurality of users in relation to the first service, and identify for at least one of the plurality of users, based on the respective information from the third-party data source, a request to change the first service. Based on the identified user action and the identified request to change the first service, the media guidance application may generate a model to determine a likelihood that a user will change the first service.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority benefit under 35 U.S.C. §119(e) from U.S. provisional application No. 61/985,149, filed on Apr. 28, 2014. The aforementioned, earlier-filed application is hereby incorporated by reference herein in its entirety.

BACKGROUND

Users have access to content from a variety of sources (e.g., various cable providers, various satellite providers, various Internet sources, etc.). Some of these sources offer a variety of services (e.g., premium services such as on-demand content purchasing, premium channels, higher speed Internet access, telephone services, etc.). Because of the variety of services available and the availability of content from various sources, users are often disconnecting or switching sources and/or services. Traditional systems lack a way to determine the likelihood a user is about to disconnect or switch sources/services to try to prevent the user from doing so (e.g., by providing discounts and/or advertisements).

SUMMARY

Viewer data (e.g., subscriber analytics) helps service providers attract and retain customers by identifying those who may have a propensity to add products and services or who may churn based on viewing behaviors and subscription information. The media guidance application may match viewership data with subscriber billing records to model and evaluate the likely behavior of subscribers and assign a propensity score. These scores are useful for identifying cross-sell and up-sell opportunities as well as focusing marketing activities on the most high-value subscribers.

Accordingly, systems and methods are described herein for determining a likelihood that a user will change a service. In some aspects, a media guidance application may perform a method for determining a likelihood that a user will change a service. The media guidance application may identify a first service to which a user is subscribed. In some embodiments, the media guidance application may identify the first service by accessing user subscription data or a user profile. The media guidance application may receive information from a third-party data source corresponding to the user. In some embodiments, the third-party data source may be a social media data source, such as a social media website, and the information from the third-party data source corresponding to the user may comprise data from the user's social media profile. The media guidance application may identify, based on the information from the third-party data source, data indicating a negative interest in the first service, and based on the identified data, the media guidance application may calculate a likelihood that the user will change the first service.

In some embodiments, the media guidance application may identify negative interest in the first service by identifying negative feedback of the first service made by the user. For example, the user may post a negative review on a social media website or provide negative comments to a market survey. The media guidance application may also identify negative interest in the first service by identifying a relative lack of comments about the first service. For example, the media guidance application may identify, from the information from the third-party data source, comments made by the user that include the first service, determine a first time period based on the identified comments, and calculate a first frequency of the identified comments during the first time period. The media guidance application may then identify, from the information from the third-party data source, comments made by the user in a second time period subsequent to the first time period, calculate a second frequency of the identified comments made by the user in the second time period, and determine that the second frequency is less than the first frequency. Thus, the media guidance application may detect whether a user stops commenting about the service or posts comments less frequently than before.

In some embodiments, the media guidance application may identify negative interest in the first service through individuals connected to the user through a social network maintained by the third-party data source (e.g., Facebook friends). The media guidance application may identify an individual connected to the user through a social network maintained by the third-party data source and identify, from the information from the third-party data source, negative feedback of the first service made by the individual. The media guidance application may also identify a relative lack of comments by the individual connected to the user through the social network. For example, the media guidance application may identify an individual connected to the user through a social network maintained by the third-party data source, identify, from the information from the third-party data source, comments made by the individual that include the first service, determine a first time period based on the identified comments, and calculate a first frequency of the identified comments during the first time period. The media guidance application may further identify, from the information from the third-party data source, comments made by the individual in a second time period subsequent to the first time period, calculate a second frequency of the identified comments made by the individual in the second time period, and determine that the second frequency is less than the first frequency. In this manner, the media guidance application may detect whether individuals in the user's social circle have stopped commenting or are commenting less frequently about the service.

In some embodiments, the media guidance application may transmit the likelihood that the user will change the first service to a service provider of the first service. The media guidance application and/or the service provider may determine whether the user has a relative high likelihood of changing or terminating the first service. For example, the media guidance application or the service provider may determine whether the calculated likelihood exceeds a threshold, indicating that the user will likely change or terminate the service in the near future. The service provider and/or the media guidance application may transmit to the user at least one of an advertisement for the first service or an offer for a discount for the first service. The advertisement or offer may be transmitted and/or presented in response to calculating the likelihood that the user will change the first service.

In some embodiments, the media guidance application may further identify a second service to which the user is not subscribed, identify, based on the information from the third-party data source, data indicating a positive interest in the second service, and update the likelihood that the user will change the first service based on the identified data indicating a positive interest in the second service. For example, the media guidance application may determine that interest in the second service is rising, and may determine that the user has a higher likelihood of changing or terminating the first service.

In another aspect, systems and methods are described herein for generating a model to determine a likelihood that a user will change a service. A media guidance application may identify a first service to which a plurality of users are subscribed, receive information from a third-party data source for each of the plurality of users, identify, based on the respective information from the third-party data source, a user action performed by each of the plurality of users in relation to the first service, and identify for at least one of the plurality of users, based on the respective information from the third-party data source, a request to change the first service. Based on the identified user action and the identified request to change the first service, the media guidance application may generate a model to determine a likelihood that a user will change the first service.

In some embodiments, the media guidance application may generate the model by calculating a probability that the identified user action is followed by the identified request to change the first service. In some embodiments, the request to change the first service may follow the identified user action after a user-identified amount of time. As an illustrative example, the media guidance application may identify a plurality of users who posted negative comments about a service. At least one user of the plurality of users requested to terminate the service. Based on this data, the media guidance application may calculate a probability that an action of posting negative comments about a service is followed by a request to terminate the service.

In some embodiments, the media guidance application may identify, based on the respective information from the third-party data source, a second user action performed by each of the plurality of users in relation to the first service, determine a pattern of action based on the first user action and the second user action, and calculate a probability that the determined pattern of action is followed by the identified request to change the first service. Continuing the illustrative example from above, the plurality of users may each exhibit a pattern of posting negative comments/feedback about the service. Based on this information, the media guidance application may calculate the probability that several negative comments are followed by a request to terminate the first service. In some embodiments, the user action is one of a playback action, negative feedback for the first service, a negative comment made about the first service, or a lack of user action for a period of time.

In some embodiments, the media guidance application may use the model to determine the likelihood that a user will change a service. The media guidance application may receive information from the third-party data source for the user, identify, based on the information from the third-party data source for the user, a user action performed by the user in relation to the first service, and compare the user action performed by the user to the user action performed by each of the plurality of users. The media guidance application may calculate, based on the comparison, a correlation factor that indicates a similarity between the user and one of the plurality of users. The media guidance application may determine, based on the correlation factor, a probability that the user will request to change the first service. For example, the media guidance application may determine that the user and the one of the plurality of users have executed one or more similar actions in a similar timeframe, and thus are likely to request similar changes to the service in the near future. In some embodiments, the media guidance application may transmit the correlation factor to a service provider of the first service.

It should be noted that the systems and/or methods described above may be applied to, or used in accordance with, other systems, methods and/or apparatuses.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the disclosure will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative example of a display screen for use in accessing media content in accordance with some embodiments of the disclosure;

FIG. 2 shows another illustrative example of a display screen used access media content in accordance with some embodiments of the disclosure;

FIG. 3 is a block diagram of an illustrative user equipment device in accordance with some embodiments of the disclosure;

FIG. 4 is a block diagram of an illustrative media system in accordance with some embodiments of the disclosure;

FIG. 5 is an illustrative system 500 for generating an indicator of the likelihood that a user will terminate access to a service or source;

FIG. 6 is a flowchart of illustrative steps for determining a likelihood that a user will change a service in accordance with some embodiments of the disclosure;

FIG. 7 is a flowchart of another set of illustrative steps for determining a likelihood that a user will change a service in accordance with some embodiments of the disclosure;

FIG. 8 is a flowchart of illustrative steps for generating a model to determine a likelihood that a user will change a service in accordance with some embodiments of the disclosure; and

FIG. 9 is a flowchart of another set of illustrative steps for generating a model to determine a likelihood that a user will change a service in accordance with some embodiments of the disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS

The amount of content available to users in any given content delivery system can be substantial. Consequently, many users desire a form of media guidance through an interface that allows users to efficiently navigate content selections and easily identify content that they may desire. An application that provides such guidance is referred to herein as an interactive media guidance application or, sometimes, a media guidance application or a guidance application.

Systems and methods are described herein for determining a likelihood that a user will change a service. In some aspects, a media guidance application may perform a method for determining a likelihood that a user will change a service. The media guidance application may identify a first service to which a user is subscribed. In some embodiments, the media guidance application may identify the first service by accessing user subscription data or a user profile. The media guidance application may receive information from a third-party data source corresponding to the user. In some embodiments, the third-party data source may be a social media data source, such as a social media website, and the information from the third-party data source corresponding to the user may comprise data from the user's social media profile. The media guidance application may identify, based on the information from the third-party data source, data indicating a negative interest in the first service, and based on the identified data, the media guidance application may calculate a likelihood that the user will change the first service.

Interactive media guidance applications may take various forms depending on the content for which they provide guidance. One typical type of media guidance application is an interactive television program guide. Interactive television program guides (sometimes referred to as electronic program guides) are well-known guidance applications that, among other things, allow users to navigate among and locate many types of content or media assets. Interactive media guidance applications may generate graphical user interface screens that enable a user to navigate among, locate and select content. As referred to herein, the terms “media asset” and “content” should be understood to mean an electronically consumable user asset, such as television programming, as well as pay-per-view programs, on-demand programs (as in video-on-demand (VOD) systems), Internet content (e.g., streaming content, downloadable content, Webcasts, etc.), video clips, audio, content information, pictures, rotating images, text documents, playlists, websites, articles, books, electronic books, blogs, advertisements, chat sessions, social media, applications, games, and/or any other media or multimedia and/or combination of the same. Guidance applications also allow users to navigate among and locate content. As referred to herein, the term “multimedia” should be understood to mean content that utilizes at least two different content forms described above, for example, text, audio, images, video, or interactivity content forms. Content may be recorded, played, displayed or accessed by user equipment devices, but can also be part of a live performance.

The media guidance application and/or any instructions for performing any of the embodiments discussed herein may be encoded on computer readable media. Computer readable media includes any media capable of storing data. The computer readable media may be transitory, including, but not limited to, propagating electrical or electromagnetic signals, or may be non-transitory including, but not limited to, volatile and non-volatile computer memory or storage devices such as a hard disk, floppy disk, USB drive, DVD, CD, media cards, register memory, processor caches, Random Access Memory (“RAM”), etc.

With the advent of the Internet, mobile computing, and high-speed wireless networks, users are accessing media on user equipment devices on which they traditionally did not. As referred to herein, the phrase “user equipment device,” “user equipment,” “user device,” “electronic device,” “electronic equipment,” “media equipment device,” or “media device” should be understood to mean any device for accessing the content described above, such as a television, a Smart TV, a set-top box, an integrated receiver decoder (IRD) for handling satellite television, a digital storage device, a digital media receiver (DMR), a digital media adapter (DMA), a streaming media device, a DVD player, a DVD recorder, a connected DVD, a local media server, a BLU-RAY player, a BLU-RAY recorder, a personal computer (PC), a laptop computer, a tablet computer, a WebTV box, a personal computer television (PC/TV), a PC media server, a PC media center, a hand-held computer, a stationary telephone, a personal digital assistant (PDA), a mobile telephone, a portable video player, a portable music player, a portable gaming machine, a smart phone, or any other television equipment, computing equipment, or wireless device, and/or combination of the same. In some embodiments, the user equipment device may have a front facing screen and a rear facing screen, multiple front screens, or multiple angled screens. In some embodiments, the user equipment device may have a front facing camera and/or a rear facing camera. On these user equipment devices, users may be able to navigate among and locate the same content available through a television. Consequently, media guidance may be available on these devices, as well. The guidance provided may be for content available only through a television, for content available only through one or more of other types of user equipment devices, or for content available both through a television and one or more of the other types of user equipment devices. The media guidance applications may be provided as on-line applications (i.e., provided on a web-site), or as stand-alone applications or clients on user equipment devices. Various devices and platforms that may implement media guidance applications are described in more detail below.

One of the functions of the media guidance application is to provide media guidance data to users. As referred to herein, the phrase “media guidance data” or “guidance data” should be understood to mean any data related to content or data used in operating the guidance application. For example, the guidance data may include program information, guidance application settings, user preferences, user profile information, media listings, media-related information (e.g., broadcast times, broadcast channels, titles, descriptions, ratings information (e.g., parental control ratings, critic's ratings, etc.), genre or category information, actor information, logo data for broadcasters' or providers' logos, etc.), media format (e.g., standard definition, high definition, 3D, etc.), advertisement information (e.g., text, images, media clips, etc.), on-demand information, blogs, websites, and any other type of guidance data that is helpful for a user to navigate among and locate desired content selections.

FIGS. 1-2 show illustrative display screens that may be used to provide media guidance data and media assets. The display screens shown in FIGS. 1-2 may be implemented on any suitable user equipment device or platform. While the displays of FIGS. 1-2 are illustrated as full screen displays, they may also be fully or partially overlaid over content being displayed. A user may indicate a desire to access content information by selecting a selectable option provided in a display screen (e.g., a menu option, a listings option, an icon, a hyperlink, etc.) or pressing a dedicated button (e.g., a GUIDE button) on a remote control or other user input interface or device. In response to the user's indication, the media guidance application may provide a display screen with media guidance data organized in one of several ways, such as by time and channel in a grid, by time, by channel, by source, by content type, by category (e.g., movies, sports, news, children, or other categories of programming), or other predefined, user-defined, or other organization criterion.

FIG. 1 shows illustrative grid program listings display 100 arranged by time and channel that also enables access to different types of content in a single display. Display 100 may include grid 102 with: (1) a column of channel/content type identifiers 104, where each channel/content type identifier (which is a cell in the column) identifies a different channel or content type available; and (2) a row of time identifiers 106, where each time identifier (which is a cell in the row) identifies a time block of programming. Grid 102 also includes cells of program listings, such as program listing 108, where each listing provides the title of the program provided on the listing's associated channel and time. With a user input device, a user can select program listings by moving highlight region 110. Information relating to the program listing selected by highlight region 110 may be provided in program information region 112. Region 112 may include, for example, the program title, the program description, the time the program is provided (if applicable), the channel the program is on (if applicable), the program's rating, and other desired information.

In addition to providing access to linear programming (e.g., content that is scheduled to be transmitted to a plurality of user equipment devices at a predetermined time and is provided according to a schedule), the media guidance application also provides access to non-linear programming (e.g., content accessible to a user equipment device at any time and is not provided according to a schedule). Non-linear programming may include content from different content sources including on-demand content (e.g., VOD), Internet content (e.g., streaming media, downloadable media, etc.), locally stored content (e.g., content stored on any user equipment device described above or other storage device), or other time-independent content. On-demand content may include movies or any other content provided by a particular content provider (e.g., HBO On Demand providing “The Sopranos” and “Curb Your Enthusiasm”). HBO ON DEMAND is a service mark owned by Time Warner Company L.P. et al. and THE SOPRANOS and CURB YOUR ENTHUSIASM are trademarks owned by the Home Box Office, Inc. Internet content may include web events, such as a chat session or Webcast, or content available on-demand as streaming content or downloadable content through an Internet web site or other Internet access (e.g. FTP).

Grid 102 may provide media guidance data for non-linear programming including on-demand listing 114, recorded content listing 116, and Internet content listing 118. A display combining media guidance data for content from different types of content sources is sometimes referred to as a “mixed-media” display. Various permutations of the types of media guidance data that may be displayed that are different than display 100 may be based on user selection or guidance application definition (e.g., a display of only recorded and broadcast listings, only on-demand and broadcast listings, etc.). As illustrated, listings 114, 116, and 118 are shown as spanning the entire time block displayed in grid 102 to indicate that selection of these listings may provide access to a display dedicated to on-demand listings, recorded listings, or Internet listings, respectively. In some embodiments, listings for these content types may be included directly in grid 102. Additional media guidance data may be displayed in response to the user selecting one of the navigational icons 120. (Pressing an arrow key on a user input device may affect the display in a similar manner as selecting navigational icons 120.)

Display 100 may also include video region 122, advertisement 124, and options region 126. Video region 122 may allow the user to view and/or preview programs that are currently available, will be available, or were available to the user. The content of video region 122 may correspond to, or be independent from, one of the listings displayed in grid 102. Grid displays including a video region are sometimes referred to as picture-in-guide (PIG) displays. PIG displays and their functionalities are described in greater detail in Satterfield et al. U.S. Pat. No. 6,564,378, issued May 13, 2003 and Yuen et al. U.S. Pat. No. 6,239,794, issued May 29, 2001, which are hereby incorporated by reference herein in their entireties. PIG displays may be included in other media guidance application display screens of the embodiments described herein.

Advertisement 124 may provide an advertisement for content that, depending on a viewer's access rights (e.g., for subscription programming), is currently available for viewing, will be available for viewing in the future, or may never become available for viewing, and may correspond to or be unrelated to one or more of the content listings in grid 102. Advertisement 124 may also be for products or services related or unrelated to the content displayed in grid 102. Advertisement 124 may be selectable and provide further information about content, provide information about a product or a service, enable purchasing of content, a product, or a service, provide content relating to the advertisement, etc. Advertisement 124 may be targeted based on a user's profile/preferences, monitored user activity, the type of display provided, or on other suitable targeted advertisement bases.

While advertisement 124 is shown as rectangular or banner shaped, advertisements may be provided in any suitable size, shape, and location in a guidance application display. For example, advertisement 124 may be provided as a rectangular shape that is horizontally adjacent to grid 102. This is sometimes referred to as a panel advertisement. In addition, advertisements may be overlaid over content or a guidance application display or embedded within a display. Advertisements may also include text, images, rotating images, video clips, or other types of content described above. Advertisements may be stored in a user equipment device having a guidance application, in a database connected to the user equipment, in a remote location (including streaming media servers), or on other storage means, or a combination of these locations. Providing advertisements in a media guidance application is discussed in greater detail in, for example, Knudson et al., U.S. Patent Application Publication No. 2003/0110499, filed Jan. 17, 2003; Ward, III et al. U.S. Pat. No. 6,756,997, issued Jun. 29, 2004; and Schein et al. U.S. Pat. No. 6,388,714, issued May 14, 2002, which are hereby incorporated by reference herein in their entireties. It will be appreciated that advertisements may be included in other media guidance application display screens of the embodiments described herein.

Options region 126 may allow the user to access different types of content, media guidance application displays, and/or media guidance application features. Options region 126 may be part of display 100 (and other display screens described herein), or may be invoked by a user by selecting an on-screen option or pressing a dedicated or assignable button on a user input device. The selectable options within options region 126 may concern features related to program listings in grid 102 or may include options available from a main menu display. Features related to program listings may include searching for other air times or ways of receiving a program, recording a program, enabling series recording of a program, setting program and/or channel as a favorite, purchasing a program, or other features. Options available from a main menu display may include search options, VOD options, parental control options, Internet options, cloud-based options, device synchronization options, second screen device options, options to access various types of media guidance data displays, options to subscribe to a premium service, options to edit a user's profile, options to access a browse overlay, or other options.

The media guidance application may be personalized based on a user's preferences. A personalized media guidance application allows a user to customize displays and features to create a personalized “experience” with the media guidance application. This personalized experience may be created by allowing a user to input these customizations and/or by the media guidance application monitoring user activity to determine various user preferences. Users may access their personalized guidance application by logging in or otherwise identifying themselves to the guidance application. Customization of the media guidance application may be made in accordance with a user profile. The customizations may include varying presentation schemes (e.g., color scheme of displays, font size of text, etc.), aspects of content listings displayed (e.g., only HDTV or only 3D programming, user-specified broadcast channels based on favorite channel selections, re-ordering the display of channels, recommended content, etc.), desired recording features (e.g., recording or series recordings for particular users, recording quality, etc.), parental control settings, customized presentation of Internet content (e.g., presentation of social media content, e-mail, electronically delivered articles, etc.) and other desired customizations.

The media guidance application may allow a user to provide user profile information or may automatically compile user profile information. The media guidance application may, for example, monitor the content the user accesses and/or other interactions the user may have with the guidance application. Additionally, the media guidance application may obtain all or part of other user profiles that are related to a particular user (e.g., from other web sites on the Internet the user accesses, such as www.allrovi.com, from other media guidance applications the user accesses, from other interactive applications the user accesses, from another user equipment device of the user, etc.), and/or obtain information about the user from other sources that the media guidance application may access. As a result, a user can be provided with a unified guidance application experience across the user's different user equipment devices. This type of user experience is described in greater detail below in connection with FIG. 4. Additional personalized media guidance application features are described in greater detail in Ellis et al., U.S. Patent Application Publication No. 2005/0251827, filed Jul. 11, 2005, Boyer et al., U.S. Pat. No. 7,165,098, issued Jan. 16, 2007, and Ellis et al., U.S. Patent Application Publication No. 2002/0174430, filed Feb. 21, 2002, which are hereby incorporated by reference herein in their entireties.

Another display arrangement for providing media guidance is shown in FIG. 2. Video mosaic display 200 includes selectable options 202 for content information organized based on content type, genre, and/or other organization criterion. In display 200, television listings option 204 is selected, thus providing listings 206, 208, 210, and 212 as broadcast program listings. In display 200 the listings may provide graphical images including cover art, still images from the content, video clip previews, live video from the content, or other types of content that indicate to a user the content being described by the media guidance data in the listing. Each of the graphical listings may also be accompanied by text to provide further information about the content associated with the listing. For example, listing 208 may include more than one portion, including media portion 214 and text portion 216. Media portion 214 and/or text portion 216 may be selectable to view content in full-screen or to view information related to the content displayed in media portion 214 (e.g., to view listings for the channel that the video is displayed on).

The listings in display 200 are of different sizes (i.e., listing 206 is larger than listings 208, 210, and 212), but if desired, all the listings may be the same size. Listings may be of different sizes or graphically accentuated to indicate degrees of interest to the user or to emphasize certain content, as desired by the content provider or based on user preferences. Various systems and methods for graphically accentuating content listings are discussed in, for example, Yates, U.S. Patent Application Publication No. 2010/0153885, filed Dec. 29, 2005, which is hereby incorporated by reference herein in its entirety.

Users may access content and the media guidance application (and its display screens described above and below) from one or more of their user equipment devices. FIG. 3 shows a generalized embodiment of illustrative user equipment device 300. More specific implementations of user equipment devices are discussed below in connection with FIG. 4. User equipment device 300 may receive content and data via input/output (hereinafter “I/O”) path 302. I/O path 302 may provide content (e.g., broadcast programming, on-demand programming, Internet content, content available over a local area network (LAN) or wide area network (WAN), and/or other content) and data to control circuitry 304, which includes processing circuitry 306 and storage 308. Control circuitry 304 may be used to send and receive commands, requests, and other suitable data using I/O path 302. I/O path 302 may connect control circuitry 304 (and specifically processing circuitry 306) to one or more communications paths (described below). I/O functions may be provided by one or more of these communications paths, but are shown as a single path in FIG. 3 to avoid overcomplicating the drawing.

Control circuitry 304 may be based on any suitable processing circuitry such as processing circuitry 306. As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc., and may include a multi-core processor (e.g., dual-core, quad-core, hexa-core, or any suitable number of cores) or supercomputer. In some embodiments, processing circuitry may be distributed across multiple separate processors or processing units, for example, multiple of the same type of processing units (e.g., two Intel Core i7 processors) or multiple different processors (e.g., an Intel Core i5 processor and an Intel Core i7 processor). In some embodiments, control circuitry 304 executes instructions for a media guidance application stored in memory (i.e., storage 308). Specifically, control circuitry 304 may be instructed by the media guidance application to perform the functions discussed above and below. For example, the media guidance application may provide instructions to control circuitry 304 to generate the media guidance displays. In some implementations, any action performed by control circuitry 304 may be based on instructions received from the media guidance application.

In client-server based embodiments, control circuitry 304 may include communications circuitry suitable for communicating with a guidance application server or other networks or servers. The instructions for carrying out the above mentioned functionality may be stored on the guidance application server. Communications circuitry may include a cable modem, an integrated services digital network (ISDN) modem, a digital subscriber line (DSL) modem, a telephone modem, Ethernet card, or a wireless modem for communications with other equipment, or any other suitable communications circuitry. Such communications may involve the Internet or any other suitable communications networks or paths (which is described in more detail in connection with FIG. 4). In addition, communications circuitry may include circuitry that enables peer-to-peer communication of user equipment devices, or communication of user equipment devices in locations remote from each other (described in more detail below).

Memory may be an electronic storage device provided as storage 308 that is part of control circuitry 304. As referred to herein, the phrase “electronic storage device” or “storage device” should be understood to mean any device for storing electronic data, computer software, or firmware, such as random-access memory, read-only memory, hard drives, optical drives, digital video disc (DVD) recorders, compact disc (CD) recorders, BLU-RAY disc (BD) recorders, BLU-RAY 3D disc recorders, digital video recorders (DVR, sometimes called a personal video recorder, or PVR), solid state devices, quantum storage devices, gaming consoles, gaming media, or any other suitable fixed or removable storage devices, and/or any combination of the same. Storage 308 may be used to store various types of content described herein as well as media guidance data described above. Nonvolatile memory may also be used (e.g., to launch a boot-up routine and other instructions). Cloud-based storage, described in relation to FIG. 4, may be used to supplement storage 308 or instead of storage 308. Storage 308 may include a logger which stores one or more user actions and/or a timestamp corresponding to each action. Control circuitry 304 may use communication circuitry to periodically transmit this data using I/O path 302. For example, control circuitry 304 may transmit logs of user actions to media content source 416 or media guidance data source 418, discussed further below in relation to FIG. 4.

Control circuitry 304 may include video generating circuitry and tuning circuitry, such as one or more analog tuners, one or more MPEG-2 decoders or other digital decoding circuitry, high-definition tuners, or any other suitable tuning or video circuits or combinations of such circuits. Encoding circuitry (e.g., for converting over-the-air, analog, or digital signals to MPEG signals for storage) may also be provided. Control circuitry 304 may also include scaler circuitry for upconverting and downconverting content into the preferred output format of the user equipment 300. Circuitry 304 may also include digital-to-analog converter circuitry and analog-to-digital converter circuitry for converting between digital and analog signals. The tuning and encoding circuitry may be used by the user equipment device to receive and to display, to play, or to record content. The tuning and encoding circuitry may also be used to receive guidance data. The circuitry described herein, including for example, the tuning, video generating, encoding, decoding, encrypting, decrypting, scaler, and analog/digital circuitry, may be implemented using software running on one or more general purpose or specialized processors. Multiple tuners may be provided to handle simultaneous tuning functions (e.g., watch and record functions, picture-in-picture (PIP) functions, multiple-tuner recording, etc.). If storage 308 is provided as a separate device from user equipment 300, the tuning and encoding circuitry (including multiple tuners) may be associated with storage 308.

A user may send instructions to control circuitry 304 using user input interface 310. User input interface 310 may be any suitable user interface, such as a remote control, mouse, trackball, keypad, keyboard, touch screen, touchpad, stylus input, joystick, voice recognition interface, or other user input interfaces. Display 312 may be provided as a stand-alone device or integrated with other elements of user equipment device 300. For example, display 312 may be a touchscreen or touch-sensitive display. In such circumstances, user input interface 312 may be integrated with or combined with display 312. Display 312 may be one or more of a monitor, a television, a liquid crystal display (LCD) for a mobile device, amorphous silicon display, low temperature poly silicon display, electronic ink display, electrophoretic display, active matrix display, electro-wetting display, electrofluidic display, cathode ray tube display, light-emitting diode display, electroluminescent display, plasma display panel, high-performance addressing display, thin-film transistor display, organic light-emitting diode display, surface-conduction electron-emitter display (SED), laser television, carbon nanotubes, quantum dot display, interferometric modulator display, or any other suitable equipment for displaying visual images. In some embodiments, display 312 may be HDTV-capable. In some embodiments, display 312 may be a 3D display, and the interactive media guidance application and any suitable content may be displayed in 3D. A video card or graphics card may generate the output to the display 312. The video card may offer various functions such as accelerated rendering of 3D scenes and 2D graphics, MPEG-2/MPEG-4 decoding, TV output, or the ability to connect multiple monitors. The video card may be any processing circuitry described above in relation to control circuitry 304. The video card may be integrated with the control circuitry 304. Speakers 314 may be provided as integrated with other elements of user equipment device 300 or may be stand-alone units. The audio component of videos and other content displayed on display 312 may be played through speakers 314. In some embodiments, the audio may be distributed to a receiver (not shown), which processes and outputs the audio via speakers 314.

The guidance application may be implemented using any suitable architecture. For example, it may be a stand-alone application wholly implemented on user equipment device 300. In such an approach, instructions of the application are stored locally (e.g., in storage 308), and data for use by the application is downloaded on a periodic basis (e.g., from an out-of-band feed, from an Internet resource, or using another suitable approach). Control circuitry 304 may retrieve instructions of the application from storage 308 and process the instructions to generate any of the displays discussed herein. Based on the processed instructions, control circuitry 304 may determine what action to perform when input is received from input interface 310. For example, movement of a cursor on a display up/down may be indicated by the processed instructions when input interface 310 indicates that an up/down button was selected.

In some embodiments, the media guidance application is a client-server based application. Data for use by a thick or thin client implemented on user equipment device 300 is retrieved on-demand by issuing requests to a server remote to the user equipment device 300. In one example of a client-server based guidance application, control circuitry 304 runs a web browser that interprets web pages provided by a remote server. For example, the remote server may store the instructions for the application in a storage device. The remote server may process the stored instructions using circuitry (e.g., control circuitry 304) and generate the displays discussed above and below. The client device may receive the displays generated by the remote server and may display the content of the displays locally on equipment device 300. This way, the processing of the instructions is performed remotely by the server while the resulting displays are provided locally on equipment device 300. Equipment device 300 may receive inputs from the user via input interface 310 and transmit those inputs to the remote server for processing and generating the corresponding displays. For example, equipment device 300 may transmit a communication to the remote server indicating that an up/down button was selected via input interface 310. The remote server may process instructions in accordance with that input and generate a display of the application corresponding to the input (e.g., a display that moves a cursor up/down). The generated display is then transmitted to equipment device 300 for presentation to the user.

In some embodiments, the media guidance application is downloaded and interpreted or otherwise run by an interpreter or virtual machine (run by control circuitry 304). In some embodiments, the guidance application may be encoded in the ETV Binary Interchange Format (EBIF), received by control circuitry 304 as part of a suitable feed, and interpreted by a user agent running on control circuitry 304. For example, the guidance application may be an EBIF application. In some embodiments, the guidance application may be defined by a series of JAVA-based files that are received and run by a local virtual machine or other suitable middleware executed by control circuitry 304. In some of such embodiments (e.g., those employing MPEG-2 or other digital media encoding schemes), the guidance application may be, for example, encoded and transmitted in an MPEG-2 object carousel with the MPEG audio and video packets of a program.

User equipment device 300 of FIG. 3 can be implemented in system 400 of FIG. 4 as user television equipment 402, user computer equipment 404, wireless user communications device 406, or any other type of user equipment suitable for accessing content, such as a non-portable gaming machine. For simplicity, these devices may be referred to herein collectively as user equipment or user equipment devices, and may be substantially similar to user equipment devices described above. User equipment devices, on which a media guidance application may be implemented, may function as a standalone device or may be part of a network of devices. Various network configurations of devices may be implemented and are discussed in more detail below.

A user equipment device utilizing at least some of the system features described above in connection with FIG. 3 may not be classified solely as user television equipment 402, user computer equipment 404, or a wireless user communications device 406. For example, user television equipment 402 may, like some user computer equipment 404, be Internet-enabled allowing for access to Internet content, while user computer equipment 404 may, like some television equipment 402, include a tuner allowing for access to television programming. The media guidance application may have the same layout on various different types of user equipment or may be tailored to the display capabilities of the user equipment. For example, on user computer equipment 404, the guidance application may be provided as a web site accessed by a web browser. In another example, the guidance application may be scaled down for wireless user communications devices 406.

In system 400, there is typically more than one of each type of user equipment device but only one of each is shown in FIG. 4 to avoid overcomplicating the drawing. In addition, each user may utilize more than one type of user equipment device and also more than one of each type of user equipment device.

In some embodiments, a user equipment device (e.g., user television equipment 402, user computer equipment 404, wireless user communications device 406) may be referred to as a “second screen device.” For example, a second screen device may supplement content presented on a first user equipment device. The content presented on the second screen device may be any suitable content that supplements the content presented on the first device. In some embodiments, the second screen device provides an interface for adjusting settings and display preferences of the first device. In some embodiments, the second screen device is configured for interacting with other second screen devices or for interacting with a social network. The second screen device can be located in the same room as the first device, a different room from the first device but in the same house or building, or in a different building from the first device.

The user may also set various settings to maintain consistent media guidance application settings across in-home devices and remote devices. Settings include those described herein, as well as channel and program favorites, programming preferences that the guidance application utilizes to make programming recommendations, display preferences, and other desirable guidance settings. For example, if a user sets a channel as a favorite on, for example, the web site www.allrovi.com on their personal computer at their office, the same channel would appear as a favorite on the user's in-home devices (e.g., user television equipment and user computer equipment) as well as the user's mobile devices, if desired. Therefore, changes made on one user equipment device can change the guidance experience on another user equipment device, regardless of whether they are the same or a different type of user equipment device. In addition, the changes made may be based on settings input by a user, as well as user activity monitored by the guidance application.

The user equipment devices may be coupled to communications network 414. Namely, user television equipment 402, user computer equipment 404, and wireless user communications device 406 are coupled to communications network 414 via communications paths 408, 410, and 412, respectively. Communications network 414 may be one or more networks including the Internet, a mobile phone network, mobile voice or data network (e.g., a 4G or LTE network), cable network, public switched telephone network, or other types of communications network or combinations of communications networks. Paths 408, 410, and 412 may separately or together include one or more communications paths, such as, a satellite path, a fiber-optic path, a cable path, a path that supports Internet communications (e.g., IPTV), free-space connections (e.g., for broadcast or other wireless signals), or any other suitable wired or wireless communications path or combination of such paths. Path 412 is drawn with dotted lines to indicate that in the exemplary embodiment shown in FIG. 4 it is a wireless path and paths 408 and 410 are drawn as solid lines to indicate they are wired paths (although these paths may be wireless paths, if desired). Communications with the user equipment devices may be provided by one or more of these communications paths, but are shown as a single path in FIG. 4 to avoid overcomplicating the drawing.

Although communications paths are not drawn between user equipment devices, these devices may communicate directly with each other via communication paths, such as those described above in connection with paths 408, 410, and 412, as well as other short-range point-to-point communication paths, such as USB cables, IEEE 1394 cables, wireless paths (e.g., Bluetooth, infrared, IEEE 802-11x, etc.), or other short-range communication via wired or wireless paths. BLUETOOTH is a certification mark owned by Bluetooth SIG, INC. The user equipment devices may also communicate with each other directly through an indirect path via communications network 414.

System 400 includes content source 416 and media guidance data source 418 coupled to communications network 414 via communication paths 420 and 422, respectively. Paths 420 and 422 may include any of the communication paths described above in connection with paths 408, 410, and 412. Communications with the content source 416 and media guidance data source 418 may be exchanged over one or more communications paths, but are shown as a single path in FIG. 4 to avoid overcomplicating the drawing. In addition, there may be more than one of each of content source 416 and media guidance data source 418, but only one of each is shown in FIG. 4 to avoid overcomplicating the drawing. (The different types of each of these sources are discussed below.) If desired, content source 416 and media guidance data source 418 may be integrated as one source device. Although communications between sources 416 and 418 with user equipment devices 402, 404, and 406 are shown as through communications network 414, in some embodiments, sources 416 and 418 may communicate directly with user equipment devices 402, 404, and 406 via communication paths (not shown) such as those described above in connection with paths 408, 410, and 412.

Content source 416 may include one or more types of content distribution equipment including a television distribution facility, cable system headend, satellite distribution facility, programming sources (e.g., television broadcasters, such as NBC, ABC, HBO, etc.), intermediate distribution facilities and/or servers, Internet providers, on-demand media servers, and other content providers. NBC is a trademark owned by the National Broadcasting Company, Inc., ABC is a trademark owned by the American Broadcasting Company, Inc., and HBO is a trademark owned by the Home Box Office, Inc. Content source 416 may be the originator of content (e.g., a television broadcaster, a Webcast provider, etc.) or may not be the originator of content (e.g., an on-demand content provider, an Internet provider of content of broadcast programs for downloading, etc.). Content source 416 may include cable sources, satellite providers, on-demand providers, Internet providers, over-the-top content providers, or other providers of content. Content source 416 may also include a remote media server used to store different types of content (including video content selected by a user), in a location remote from any of the user equipment devices. Systems and methods for remote storage of content, and providing remotely stored content to user equipment are discussed in greater detail in connection with Ellis et al., U.S. Pat. No. 7,761,892, issued Jul. 20, 2010, which is hereby incorporated by reference herein in its entirety.

Media guidance data source 418 may provide media guidance data, such as the media guidance data described above. Media guidance data may be provided to the user equipment devices using any suitable approach. In some embodiments, the guidance application may be a stand-alone interactive television program guide that receives program guide data via a data feed (e.g., a continuous feed or trickle feed). Program schedule data and other guidance data may be provided to the user equipment on a television channel sideband, using an in-band digital signal, using an out-of-band digital signal, or by any other suitable data transmission technique. Program schedule data and other media guidance data may be provided to user equipment on multiple analog or digital television channels. In some embodiments, media guidance data source 418 may be a social media data source that maintains a social network. The social media data source may maintain profiles of each of a plurality of users and may allow the users to post comments, indicate status updates, or post any other activity.

In some embodiments, guidance data from media guidance data source 418 may be provided to users' equipment using a client-server approach. For example, a user equipment device may pull media guidance data from a server, or a server may push media guidance data to a user equipment device. In some embodiments, a guidance application client residing on the user's equipment may initiate sessions with source 418 to obtain guidance data when needed, e.g., when the guidance data is out of date or when the user equipment device receives a request from the user to receive data. Media guidance may be provided to the user equipment with any suitable frequency (e.g., continuously, daily, a user-specified period of time, a system-specified period of time, in response to a request from user equipment, etc.). Media guidance data source 418 may provide user equipment devices 402, 404, and 406 the media guidance application itself or software updates for the media guidance application.

In some embodiments, the media guidance data may include viewer data. For example, the viewer data may include current and/or historical user activity information (e.g., what content the user typically watches, what times of day the user watches content, whether the user interacts with a social network, at what times the user interacts with a social network to post information, what types of content the user typically watches (e.g., pay TV or free TV), mood, brain activity information, etc.). The media guidance data may also include subscription data. For example, the subscription data may identify to which sources or services a given user subscribes and/or to which sources or services the given user has previously subscribed but later terminated access (e.g., whether the user subscribes to premium channels, whether the user has added a premium level of services, whether the user has increased Internet speed). In some embodiments, the viewer data and/or the subscription data may identify patterns of a given user for a period of more than one year. The media guidance data may include a model (e.g., a survivor model) used for generating a score that indicates a likelihood a given user will terminate access to a service/source. For example, the media guidance application may process the viewer data with the subscription data using the model to generate a value or score that indicates a likelihood of whether the given user will terminate access to a particular service or source. In particular, a higher score may indicate a higher level of confidence that the user will terminate access to a particular service or source. Based on the score, the media guidance application may generate promotions and advertisements that entice the user to keep the particular service or source indicated by the score as one to which the user will likely terminate access.

Media guidance applications may be, for example, stand-alone applications implemented on user equipment devices. For example, the media guidance application may be implemented as software or a set of executable instructions which may be stored in storage 308, and executed by control circuitry 304 of a user equipment device 300. In some embodiments, media guidance applications may be client-server applications where only a client application resides on the user equipment device, and server application resides on a remote server. For example, media guidance applications may be implemented partially as a client application on control circuitry 304 of user equipment device 300 and partially on a remote server as a server application (e.g., media guidance data source 418) running on control circuitry of the remote server. When executed by control circuitry of the remote server (such as media guidance data source 418), the media guidance application may instruct the control circuitry to generate the guidance application displays and transmit the generated displays to the user equipment devices. The server application may instruct the control circuitry of the media guidance data source 418 to transmit data for storage on the user equipment. The client application may instruct control circuitry of the receiving user equipment to generate the guidance application displays.

Content and/or media guidance data delivered to user equipment devices 402, 404, and 406 may be over-the-top (OTT) content. OTT content delivery allows Internet-enabled user devices, including any user equipment device described above, to receive content that is transferred over the Internet, including any content described above, in addition to content received over cable or satellite connections. OTT content is delivered via an Internet connection provided by an Internet service provider (ISP), but a third party distributes the content. The ISP may not be responsible for the viewing abilities, copyrights, or redistribution of the content, and may only transfer IP packets provided by the OTT content provider. Examples of OTT content providers include YOUTUBE, NETFLIX, and HULU, which provide audio and video via IP packets. Youtube is a trademark owned by Google Inc., Netflix is a trademark owned by Netflix Inc., and Hulu is a trademark owned by Hulu, LLC. OTT content providers may additionally or alternatively provide media guidance data described above. In addition to content and/or media guidance data, providers of OTT content can distribute media guidance applications (e.g., web-based applications or cloud-based applications), or the content can be displayed by media guidance applications stored on the user equipment device.

Media guidance system 400 is intended to illustrate a number of approaches, or network configurations, by which user equipment devices and sources of content and guidance data may communicate with each other for the purpose of accessing content and providing media guidance. The embodiments described herein may be applied in any one or a subset of these approaches, or in a system employing other approaches for delivering content and providing media guidance. The following four approaches provide specific illustrations of the generalized example of FIG. 4.

In one approach, user equipment devices may communicate with each other within a home network. User equipment devices can communicate with each other directly via short-range point-to-point communication schemes described above, via indirect paths through a hub or other similar device provided on a home network, or via communications network 414. Each of the multiple individuals in a single home may operate different user equipment devices on the home network. As a result, it may be desirable for various media guidance information or settings to be communicated between the different user equipment devices. For example, it may be desirable for users to maintain consistent media guidance application settings on different user equipment devices within a home network, as described in greater detail in Ellis et al., U.S. patent application Ser. No. 11/179,410, filed Jul. 11, 2005. Different types of user equipment devices in a home network may also communicate with each other to transmit content. For example, a user may transmit content from user computer equipment to a portable video player or portable music player.

In a second approach, users may have multiple types of user equipment by which they access content and obtain media guidance. For example, some users may have home networks that are accessed by in-home and mobile devices. Users may control in-home devices via a media guidance application implemented on a remote device. For example, users may access an online media guidance application on a website via a personal computer at their office, or a mobile device such as a PDA or web-enabled mobile telephone. The user may set various settings (e.g., recordings, reminders, or other settings) on the online guidance application to control the user's in-home equipment. The online guide may control the user's equipment directly, or by communicating with a media guidance application on the user's in-home equipment. Various systems and methods for user equipment devices communicating, where the user equipment devices are in locations remote from each other, is discussed in, for example, Ellis et al., U.S. Pat. No. 8,046,801, issued Oct. 25, 2011, which is hereby incorporated by reference herein in its entirety.

In a third approach, users of user equipment devices inside and outside a home can use their media guidance application to communicate directly with content source 416 to access content. Specifically, within a home, users of user television equipment 402 and user computer equipment 404 may access the media guidance application to navigate among and locate desirable content. Users may also access the media guidance application outside of the home using wireless user communications devices 406 to navigate among and locate desirable content.

In a fourth approach, user equipment devices may operate in a cloud computing environment to access cloud services. In a cloud computing environment, various types of computing services for content sharing, storage or distribution (e.g., video sharing sites or social networking sites) are provided by a collection of network-accessible computing and storage resources, referred to as “the cloud.” For example, the cloud can include a collection of server computing devices, which may be located centrally or at distributed locations, that provide cloud-based services to various types of users and devices connected via a network such as the Internet via communications network 414. These cloud resources may include one or more content sources 416 and one or more media guidance data sources 418. In addition or in the alternative, the remote computing sites may include other user equipment devices, such as user television equipment 402, user computer equipment 404, and wireless user communications device 406. For example, the other user equipment devices may provide access to a stored copy of a video or a streamed video. In such embodiments, user equipment devices may operate in a peer-to-peer manner without communicating with a central server.

The cloud provides access to services, such as content storage, content sharing, or social networking services, among other examples, as well as access to any content described above, for user equipment devices. Services can be provided in the cloud through cloud computing service providers, or through other providers of online services. For example, the cloud-based services can include a content storage service, a content sharing site, a social networking site, or other services via which user-sourced content is distributed for viewing by others on connected devices. These cloud-based services may allow a user equipment device to store content to the cloud and to receive content from the cloud rather than storing content locally and accessing locally-stored content.

A user may use various content capture devices, such as camcorders, digital cameras with video mode, audio recorders, mobile phones, and handheld computing devices, to record content. The user can upload content to a content storage service on the cloud either directly, for example, from user computer equipment 404 or wireless user communications device 406 having content capture feature. Alternatively, the user can first transfer the content to a user equipment device, such as user computer equipment 404. The user equipment device storing the content uploads the content to the cloud using a data transmission service on communications network 414. In some embodiments, the user equipment device itself is a cloud resource, and other user equipment devices can access the content directly from the user equipment device on which the user stored the content.

Cloud resources may be accessed by a user equipment device using, for example, a web browser, a media guidance application, a desktop application, a mobile application, and/or any combination of access applications of the same. The user equipment device may be a cloud client that relies on cloud computing for application delivery, or the user equipment device may have some functionality without access to cloud resources. For example, some applications running on the user equipment device may be cloud applications, i.e., applications delivered as a service over the Internet, while other applications may be stored and run on the user equipment device. In some embodiments, a user device may receive content from multiple cloud resources simultaneously. For example, a user device can stream audio from one cloud resource while downloading content from a second cloud resource. Or a user device can download content from multiple cloud resources for more efficient downloading. In some embodiments, user equipment devices can use cloud resources for processing operations such as the processing operations performed by processing circuitry described in relation to FIG. 3.

FIG. 5 is an illustrative system 500 for generating an indicator of the likelihood that a user will terminate access to a service or source. The components and operation of system 500 may be implemented by circuitry or by software (e.g., the media guidance application). System 500 includes a predictive attributes engine 520, a history of billing data memory 530, a learning model engine 540, and a trained model engine 550. Predictive attributes engine 520 receives data (e.g., content attributes data 510, user equipment viewer data 512 which includes indications about what live and recorded content a user watches, on-demand data 514, online activity data 516, social network activity 518, current user attributes 526, and data from memory 530). On-demand data 514 may indicate which non-linear content the user has previously consumed or purchased. Online activity data 516 indicates what content the user consumed online (e.g., from a streaming source).

Predictive attributes engine 520 processes one or more of the data it receives to generate attributes that represent a population of users with similar activity. The generated attributes 522 are output to learning model engine 540. Learning model engine 540 receives historical outcomes 524 from history billing data memory 530. Historical outcomes 524 indicate what sources or services the user has previously subscribed to and to which sources or services the user has previous terminated access a period of time after subscribing to them. Learning model engine 540 may correlate historical attributes 522 of various users with historical outcomes 524 of those users to determine patterns that resulted in termination of access to sources or services. Historical outcomes 524 may be stored as a database for various users. The database may indicate for each user what sources or services the user has previously subscribed to and to which sources or services the user has previous terminated access a period of time after subscribing to them.

For example, learning model engine 540 may process historical outcomes 524 of a first user to identify a point at which the first user has terminated access to a source or service (e.g., unsubscribed from a premium channel). In some embodiments, the first user may be a former subscriber to a source or service (e.g., a user who completely disconnected service from a particular source, such as a former cable subscriber that switched to satellite). In such circumstances, learning model engine 540 may analyze behavior of the former subscriber corresponding to the viewing activity and subscription activity the former subscriber had before becoming the former subscriber. In particular, leaning model engine 540 may process the viewing activity and subscription activity of a former cable subscriber to determine what cable services the former subscriber subscribed to and/or terminated service from before disconnecting from cable and switching to satellite.

Learning model engine 540 may retrieve historical attributes 522 for that first user for a period of time before the user terminated access and/or a period of time after the user terminated access to detect a change in viewing activity that may have resulted in the user terminating access to the source or service. Learning model engine 540 may perform the same analysis for each other user for which data is available (e.g., in a database for historical outcomes 524) and who terminated access to the same source or service. After processing the data for each user who terminated access to the particular source or service, learning model engine 540 may identify similarities in attributes 522 of those users during the period preceding and/or following each respective user's termination of access to the source or service. Learning model 540 may store a correlation factor between the similar attributes and the particular source or service. The correction factor indicates when a subsequent user who is a subscriber to the same source or service exhibits at least some of the similar attributes, the user will likely terminate access to the source or service. The greater the number of similar attributes that the user exhibits, the larger the score that results from the correlation factor indicating a greater likelihood that the user will terminate access to the source or service. Learning model engine 540 may generate a different correlation factor for each source or service to which a set of users terminated access.

In some embodiments, learning model engine 540 may be trained on an on-going, continuous basis. In particular, learning model engine 540 may continuously process information (e.g., user activity, historical outcomes 524, and subscription information) for each subscriber or user and adapt or change the correlation factor for a given service. The updated correlation factor may be then provided to trained model engine 550. In some implementations, learning model engine 540 may update a previously determined correlation factor each time a given user or set of users terminate access to a particular source or service. For example, each time new information is stored to a database of historical outcomes 524 (e.g., each time a given user unsubscribes or disconnects from a given source or service), a signal identifying the source or service associated with the new information may be transmitted to learning model engine 540 indicating a need to re-compute or update a correlation factor corresponding to the identified source or service.

As referred to herein, the phrase “terminate access” or “termination of access” means that the source or service was disconnected by the user or requested by the user to be removed from the user's subscription plan. After terminating access to a given source or service a user has to re-subscribe (repay) for the source or service to resume access.

After a predetermined amount of time and/or after a data from a predetermined number of users has been processed by learning model engine 540, the model may be provided to trained model engine 550. Trained model engine 550 may process current attributes 526 of a given user with each of the correlation factors provided by learning model engine 540 that is associated with a source or service to which the user is a subscriber. Trained model engine 550 may output a score that represents how closely correlated the current user's attributes are with the correlation factor. The score output by trained model engine 550 may be source/service specific. A larger score indicates a greater likelihood that the user will terminate access to the source or service.

The media guidance application may process the score for a given user to target advertisements and promotions. For example, in response to determining that the score of a given user exceeds a first threshold, the media guidance application may identify the source or service associated with the score. The media guidance application may provide a promotion to the user for the source or service (e.g., allow the user to keep the subscription to the source or service at a discounted price). Alternatively, the media guidance application may provide an advertisement for content available on the source or service to the user. In some implementations, in response to determining that the score of a given user exceeds a second threshold higher than the first threshold, the media guidance application may provide a different set of promotions and/or advertisements. If the score is below a threshold, the media guidance application may avoid presenting promotions or advertisements for the source or service.

In some embodiments, in response to determining that the score of a given user is below a threshold, the media guidance application may instruct a subscriber management system to initiate a process for retaining the given user. In some embodiments, in response to determining that the score of a given user is below a threshold, the media guidance application may provide a visual alert to an operator of source or service associated with the score. The visual alert may include information that identifies the user, the source or service, and/or the score. For example, the subscriber management system may, based on the instruction from the media guidance application and the score, contact the given user (e.g., place a phone call, send a text message or email) to offer a new offer, discount on other services (e.g., packages of programming), or reduction in price of current services. The offer may be specific to the source or service for which the score is below the threshold or generic. In particular, if the score indicates that the user is likely to terminate access to a premium channel, the subscriber management system may contact the given user offering any combination of: a reduction in the current price the given user is paying for the premium channel, a discount on a new service (e.g., phone service for a cable subscriber), or a discount on a new premium channel not currently subscribed to by the given user. In some implementations, the subscriber management system may apply different offers to different users who have the same scores. The subscriber management system may determine what level of offer to a given user not only based on the score of the user but also based on information stored in historical outcomes 524 for the user and/or currently subscribed to services. For example, if first and second users have the same score for a particular service (e.g., cable) but the first user is not a subscriber to premium channels, the subscriber management system may offer the premium channels (or a set of premium channels that meet a user profile for the first user) at a discount. The second user may already be a subscriber to the premium channels and accordingly the subscriber management system may offer alternate services to the second user at a discount (e.g., phone services if the second user does not currently have phone service).

In some embodiments, trained model engine 550 may receive subscription information for a given user and user activity information. The subscription information may indicate that the user is a subscriber to a premium channel on the source (e.g., HBO on cable) and the user activity information may indicate that the given user has not viewed content from the premium channel in more than a threshold period of time (e.g., more than 2 weeks). In response, trained model engine 550 may identify a correlation factor associated with the premium channel and generate a score indicating that the user is likely to terminate access to the premium channel. The value of the score may be higher or lower based on other user activity and subscription information. For example, if the user watches content from an affiliate of the premium channel (e.g., Cinemax) which is tied to the subscription of the primary channel (e.g., HBO), then the score may be reduced.

In some embodiments, the subscription information may indicate that the user is a subscriber to a premium channel on the source (e.g., HBO on cable) and the user activity information may indicate that the given user has increased the speed of their Internet connection. In response, trained model engine 550 may identify a correlation factor associated with the premium channel and generate a score indicating that the user is likely to terminate access to the premium channel. In particular, the user may have increased Internet speed because they intend to access more content online and may not need the premium channel anymore. The score may be further increased if the above determination is made that the user has not viewed content from the premium source in more than a threshold period of time (e.g., more than 2 weeks).

In some embodiments, the subscription information may indicate that the user has purchased a predetermined number of movies from a source (e.g., a certain cable provider) and the activity information may indicate that the user watches non-premium content sources (e.g., free TV). In response, the score output by trained model engine 550 may be reduced as the user is less likely to terminate access to the source (e.g., disconnect service from the cable provider). In some implementations, the subscription information may indicate that the user is a subscriber to a cable provider and the user activity information may indicate that the given user watches new releases on-demand from the cable provider. In response, the score corresponding to the cable provider output by trained model engine 550 may be decreased as the user is unlikely to terminate access to the cable provider as there may not be an alternate source from which the user can obtain access to the new releases. In some implementations, the subscription information may indicate that the user is a subscriber to a cable provider and the user activity information may indicate that the given user does not watch many live events (e.g., linear content) and has increased the Internet speed. In response, the score corresponding to the cable provider output by trained model engine 550 may be increased as the user is likely to terminate access to the cable provider as the user may be looking to stream more content from an online source. In some implementations, the subscription information may indicate that the user is a subscriber to a cable provider and the user activity information may indicate that the given user watches many live events (e.g., linear content) and comments on a social network about the live events. In response, the score corresponding to the cable provider output by trained model engine 550 may be decreased as the user is unlikely to terminate access to the cable provider. In some implementations, the subscription information may indicate that the user is a subscriber to a cable provider and the user activity information may indicate that the given user watches a variety of content. In response, the score corresponding to the cable provider output by trained model engine 550 may be decreased as the user is unlikely to terminate access to the cable provider.

In some implementations, the subscription information may indicate that the user is a subscriber to a premium channel on the source (e.g., HBO on cable) and the user activity information may indicate that the given user has subscribed to a different premium channel (e.g., Showtime on cable). In response, the score corresponding to the HBO premium channel output by trained model engine 550 may be increased as the user is likely to terminate access to the HBO premium channel given that the user will consume content from the other premium channel. In some implementations, the subscription information may indicate that the user is a subscriber to a premium channel on the source (e.g., HBO on cable) and the user activity information may indicate that the given user only watches a certain show on the premium channel that has recently ended (e.g., the season of the show has finished). In response, the score corresponding to the HBO premium channel output by trained model engine 550 may be increased as the user is likely to terminate access to the HBO premium channel given that the user will no longer have content to consume from the premium channel.

FIG. 6 is a flowchart of illustrative steps for determining a likelihood that a user will change a service in accordance with some embodiments of the disclosure. Process 600 includes identifying a first service to which a user is subscribed at step 602, receiving information from a third-party data source corresponding to the user at step 604, identifying, based on the information from the third-party data source, data indicating a negative interest in the first service at step 606, and calculating a likelihood that the user will change the first service based on the identified data at step 608.

At step 602, a media guidance application may identify (e.g., via control circuitry 304 (FIG. 3)) a first service to which a user is subscribed. The first service may be any subscription service, including, but not limited to television services, media delivery services, premium services such as on-demand content purchasing or premium channels, Internet access, or telephone services. The media guidance application may identify a user's subscription status using any suitable method. For example, the media guidance application may store or access information associated with a user, such as a user profile, which includes information on services to which the user is subscribed. The media guidance application may also query a remote server to determine whether a user is subscribed to the first service. As discussed above in relation to FIG. 5, a user's subscription information may be stored in a memory such as history billing data memory 530, which sends the subscription information to learning model engine 540.

At step 604, the media guidance application may receive (e.g., through communication network 414 (FIG. 4) using control circuitry 304 (FIG. 3)) information from a third-party data source corresponding to the user. The third-party data source may be any suitable data source for storing information corresponding to the user. For example, the third-party data source may be a media guidance data source, such as media guidance data source 418 depicted in FIG. 4, that stores a user profile. The third-party data source may also be a social network data source that maintains a social networking application for connecting the user to other individuals in the social network. The social network data source may provide a user profile associated with the user, comments or posts that the user has uploaded onto the social network, profiles associated with individuals connected to the user via the social network, comments or posts that those associated individuals have uploaded onto the social network, or any other information associated with the user.

At step 606, the media guidance application may identify (e.g., via control circuitry 304 (FIG. 3)), based on the information from the third-party data source, data indicating a negative interest in the first service. The media guidance application may identify negative interest in any number of ways. For example, the media guidance application may identify negative feedback or comments made about the service. As an illustrative example, the media guidance application may search a user's social media comments and posts for keywords associated with the first service, such as the name or type of the first service. The media guidance application may cross-reference these words with words associated with negative feedback (e.g., “bad,” “horrible,” “never again,” etc.). The media guidance may identify negative interest in the service by identifying one or more of the words associated with negative feedback. The media guidance application may maintain statistics on the frequency of each of the keywords and words associated with negative feedback. The media guidance application may also determine the proximity of the keywords associated with the first service and the words associated with negative feedback. The media guidance application may determine whether the words associated with negative feedback occur with a frequency greater than a frequency threshold, or if the words associated with negative feedback occur within a predetermined number of words of the keywords associated with the first service. The media guidance application may identify negative interest based on either determination.

The media guidance application may also identify negative interest in the first service by identifying a relative lack of comments or feedback about a first service during a period of time. For example, the media guidance application may search a user's comments or posts for occurrences of keywords associated with the first service (e.g., the name of the service, the type of service, posts about the service, etc.) within a first time period. The media guidance application may determine a first frequency of occurrence of the keywords during the first period of time. The media guidance application may then search a user's comments or posts for keywords associated with the first service during a second time period and calculate a corresponding frequency of occurrence for the second time period. A second frequency which is less than the first frequency indicates that the user is mentioning the first service with decreasing frequency. As an illustrative example, the user may comment feverishly about a new cable service that they have just subscribed to, but gradually become less enthralled with the service. This loss of interest may be indicated by a decrease in the number of comments about the new service as time moves on.

At step 608, the media guidance application may calculate (e.g., via control circuitry 304 (FIG. 3)) a likelihood that the user will change the first service based on the identified data indicating a negative interest in the first service. The media guidance application may calculate the likelihood based on a type of the negative interest. For example, the media guidance application may initialize the likelihood that the user will change the first service to zero and increment the likelihood based on identifying occurrences of words associated with negative feedback, as discussed above in relation to step 606. Some types of negative feedback may be more or less heavily weighted when calculating the likelihood. For example, an overt statement, such as “I should probably cancel HBO” may receive a heavier weighting and thus increase the likelihood by a greater amount than a more subtle statement, such as “HBO is boring.” The media guidance application may determine the severity of a negative statement by cross-referencing keywords with a database of pre-determined words and phrases. For example, as discussed above in relation with FIG. 5, historical outcomes database 524 may store the historical actions and patterns of several users that result in the termination of services for those users. The media guidance application may compare the actions and/or statement of the user to the historical data in historical outcomes database 524 to determine that likelihood that the user's actions will result in a change in the first service. In some embodiments, the likelihood that the user will change the first service may be in relation to a time period. For instance, the likelihood that the user will change the first service may be provided as a percentage likelihood of termination in the next six months.

FIG. 7 is a flowchart of another set of illustrative steps for determining a likelihood that a user will change a service in accordance with some embodiments of the disclosure. Process 700 includes initializing the likelihood that the user will change a service to zero at step 702, receiving information from a third-party data source corresponding to the user at step 704, and identifying individuals connected to the user through a social network maintained by the third-party data source at step 706. One of the identified individuals is selected at step 708, and a determination is made whether negative feedback or comments have been received from the selected individual at step 710. If negative feedback or comments have been received from the selected individual, then the likelihood that the user will change the service is increased at step 712 and the process 700 continues to step 714. If negative feedback or comments have been received from the selected individual, then the process 700 continues directly to step 714. The process 700 further comprises determining a first frequency of comments during a first period and a second frequency of comments during a second period at step 714 and determining whether the second frequency is less than the first frequency at step 716. If the second frequency is less than the first frequency, then the likelihood that the user will change the service is increased at step 718. If the second frequency is not less than the first frequency, then the process 700 continues directly to step 720. The process 700 further includes determining whether unselected individuals remain, and if they do, then selecting another user of the identified individuals connected to the user through a social network maintained by the third-party data source at step 708. The process 700 further includes determining whether the likelihood that the user will change the service exceeds a threshold at step 722. If the likelihood exceeds the threshold, then at least one of an advertisement or an offer for a discount for the service may be transmitted to the user at step 724. If the likelihood does not exceed the threshold, then the process 700 ends at step 726.

At step 702, the media guidance application may initialize (e.g., via control circuitry 304 (FIG. 3)) a likelihood that the user will change a service to zero, and at step 704, the media guidance application may receive (e.g., via control circuitry 304 (FIG. 3)) information from a third-party data source corresponding to the user. Step 704 may be substantially similar to step 604 discussed above in relation to FIG. 6. At step 706, the media guidance application may identify (e.g., via control circuitry 304 (FIG. 3)) individuals connected to the user through a social network maintained by the third-party data source. For instance, the media guidance application may identify “friends” of the user connected through the social network. The media guidance application may identify individuals connected through the user through any number of links (e.g., “friends of friends”) or within a pre-determined number of links. At step 708, the media guidance application may select (e.g., via control circuitry 304 (FIG. 3)) either the user or one of the identified individuals connected to the user through the social network. At step 710, the media guidance application determines (e.g., via control circuitry 304 (FIG. 3)) whether negative feedback or comments have been received from the selected individual. Step 710 may be substantially similar to step 606 discussed above in relation to FIG. 6. For example, the media guidance application may identify comments, posts, or activity that indicate negative interest in the first service. The media guidance application may also identify a relative lack of comments or a decrease in the frequency of commenting about a first service. If such negative feedback or comments is detected, then the media guidance may increase the likelihood that the user will change the service at step 712. If negative feedback is not detected, then the media guidance application may determine a first frequency of comments during a first period and a second frequency of comments during a second period at step 714. The media guidance application compares the second frequency to the first frequency at step 716, and if the second frequency is less than the first frequency, then the media guidance application increases the likelihood that the user will change the service at step 718. If the second frequency is not less than the first frequency, then the media guidance application continues to step 720, where it determines (e.g., via control circuitry 304 (FIG. 3)) whether unselected individuals remain. If unselected individuals remain, then the media guidance application selects another individual at step 708.

If all individuals connected to the user have been analyzed, then the media guidance application determines whether the likelihood that the user will change the first service exceeds a threshold at step 720 (e.g., via control circuitry 304 (FIG. 3)). The threshold may be a pre-determined threshold set by a service provider of the first service. If the likelihood that the user will change the first service is greater than this threshold, then the media guidance application may transmit (e.g., via control circuitry 304 (FIG. 3) over communications network 414 (FIG. 4)) to the user at least one of an advertisement or an offer for a discount for the service. If the likelihood does not exceed the threshold, then the process 700 ends at step 726.

FIG. 8 is a flowchart of illustrative steps for generating a model to determine a likelihood that a user will change a service in accordance with some embodiments of the disclosure. Process 800 includes identifying a service to which a plurality of users are subscribed at step 802, receiving information from a third-party data source for each of the plurality of users at step 804, identifying, based on the information from the third-party data source, a user action performed by each of the plurality of users in relation to the service at step 806, identifying for at least one of the plurality of users, based on the respective information from the third-party data source, a request to change the first service at step 808, and generating a model to determine a likelihood that a user will change the first service based on the identified user action and the identified request to change the first service at step 810.

At step 802, a media guidance application may identify (e.g., via control circuitry 304 (FIG. 3)) a service to which a plurality of users are subscribed. The media guidance application may identify the service in step 802 in substantially the same way as in step 602 described above in relation to FIG. 6. At step 804, the media guidance application may receive (e.g., via control circuitry 304 (FIG. 3)) information from a third-party data source for each of the plurality of users at step 804. The media guidance application may receive the information from any suitable data source for storing information corresponding to the plurality of users. For example, the third-party data source may be a media guidance data source, such as media guidance data source 418 depicted in FIG. 4, that stores user profiles. The third-party data source may also be a social network data source that maintains a social networking application for connecting users to one another in the social network. The social network data source may provide user profiles associated with the users, comments or posts that the users have uploaded onto the social network, profiles associated with individuals connected to the users via the social network, comments or posts that those associated individuals have uploaded onto the social network, or any other information associated with the users.

At step 806, the media guidance application may identify (e.g., via control circuitry 304 (FIG. 3)), based on the information from the third-party data source, a user action performed by each of the plurality of users in relation to the service. For example, each of the plurality of users may have posted a bad review of the service to their social media profile. The user action may be any user action performed in relation to the service, including, but not limited to, using the service, providing feedback for the service, providing a comment about the service, upgrading a service, or terminating a service. At step 808, the media guidance application may identify (e.g., via control circuitry 304 (FIG. 3) for at least one of the plurality of users, based on the respective information from the third-party data source, a request to change the first service. The request to change the first service may comprise any change regarding the first service, including, but not limited to, subscribing to the first service, upgrading the first service, downgrading the first service, or terminating the first service. In this manner, the media guidance application may identify a plurality of users, each of which has performed a particular user action in relation to a service, and wherein at least one of the plurality of users (but perhaps not all) has requested a change to the service.

At step 808, the media guidance application may generate (e.g., via control circuitry 304 (FIG. 3)) a model to determine a likelihood that a user will change the first service based on the identified user action and the identified request to change the first service. The media guidance application may generate the model by calculating the probability that the identified user action is followed by a request to change the service. As an illustrative example, suppose 100 users posted a bad review of HBO, and of those 100 users, 50 of them terminated HBO within the next three months. The media guidance application, based on this data, may calculate that for a 101^(st) user that posts a bad review of HBO, the probability of that 101^(st) user terminating HBO within the next three months is 50%. In this manner, the media guidance application may maintain statistics and a database of user actions and historic subscription activity (e.g., new subscriptions, upgrades, downgrades, terminations, etc.) and analyze the historic subscription activity to determine correlations between user actions and historic subscription activity.

FIG. 9 is a flowchart of another set of illustrative steps for generating a model to determine a likelihood that a user will change a service in accordance with some embodiments of the disclosure. Process 900 includes receiving information from a third-party data source for each of a plurality of users at step 902, identifying actions performed by each of the plurality of users in relation to a service at step 904, determining a pattern of action based on the identified actions at step 906, identifying a request to change the service for at least one of the plurality of users at step 908, and generating a model to determine a likelihood that an action or a pattern of action is followed by a request to change the service at step 910. The process 900 further includes selecting one of the plurality of users at step 912, comparing actions performed by a current user to the actions performed by the selected user at step 914, based on the comparison, calculating a correlation factor that indicates a similarity between the current user and the selected user at step 916, and determining whether the correlation factor exceeds a threshold at step 918. If the correlation factor exceeds the threshold, then the process 900 includes using the model, determining a likelihood that the user will request to change the service at step 920 and transmitting the likelihood that the user will request to change the service to a service provider at step 922. The process 900 further includes determining whether unselected users remain, and if they do remain, selecting another user of the plurality of users at step 926. If unselected users do not remain, the process 900 ends at step 928.

At step 902, the media guidance application may receive (e.g., via control circuitry 304 (FIG. 3)) information from a third-party data source for each of a plurality of users. The step 902 may be substantially similar to the step 804 described above in relation to FIG. 8. At step 904, the media guidance application may identify actions performed by each of the plurality of users in relation to a service. The media guidance application may identify actions in step 904 in substantially the same manner as the identification in step 806 described above in relation to FIG. 8. At step 906, the media guidance application may determine (e.g., via control circuitry 304 (FIG. 3)) a pattern of action based on the identified actions at step 906. The pattern of action may be any group of two or more user actions that the plurality of users have performed. The two or more user actions may be performed in sequence or out of sequence. At step 908, the media guidance application may identify (e.g., via control circuitry 304 (FIG. 3)) a request to change the service for at least one of the plurality of users. The step 908 may be substantially similar to step 808 described above in relation to FIG. 8. As an illustrative example, a several users may exhibit a pattern of posting a series of negative comments about a service, then terminating the service. The media guidance application, based on this data, may determine that the series of negative comments constitutes a pattern of action that leads to a higher probability of terminating the service.

At step 910, the media guidance application may generate (e.g., via control circuitry 304 (FIG. 3)) a model to determine a likelihood that an action or a pattern of action is followed by a request to change the service. The step 910 may be substantially similar to the step 810 described above in relation to FIG. 8. In steps 912-916, the media guidance application may analyze a new user based on the data used to generate the model. The media guidance application may compare the actions of the new user to the actions of the plurality of users to determine a user of the plurality of users that closely matches the new user. At step 912, the media guidance application may select (e.g., via control circuitry 304 (FIG. 3)) one of the plurality of users. At step 914, the media guidance application may compare (e.g., via control circuitry 304 (FIG. 3)) actions performed by a current user to the actions performed by the selected user. For example, the historic actions of the current user (i.e., new user, user being analyzed) in relation to the service may be stored by historic billing data memory 530. The historic billing data memory 530 and/or the generated model may also store historic actions of the selected user of the plurality of users. Both sets of actions may be compared to determine any similar actions or pattern of actions. At step 916, the media guidance application may, based on the comparison, calculate (e.g., via control circuitry 304 (FIG. 3)) a correlation factor that indicates a similarity between the current user and the selected user. The correlation factor may be calculated in any suitable manner to provide a normalized value indicating the similarity between the current user and the selected user of the plurality of users. For example, the correlation factor may calculate a percentage of the number of total actions performed that were common to both the current user and the selected user of the plurality of users. The correlation factor may be calculated by weighting different user actions differently. For example, a bad review of the service may be weighted more heavily than a negative comment of the service.

At step 918, the media guidance application may determine (e.g., via control circuitry 304 (FIG. 3)) whether the correlation factor exceeds a threshold. The threshold may be a pre-determine threshold set by a service provider of the service. If the correlation factor exceeds the threshold, then the process media guidance application may determine (e.g., via control circuitry 304 (FIG. 3)) using the model, a likelihood that the user will request to change the service. The media guidance may determine the likelihood that the current user will request to change the service based at least in part on whether the selected user of the plurality of users changed the service and the correlation factor. In some embodiments, the media guidance application determine whether the selected user requested a change to the service, and if the selected user did request a change to the service, then the media guidance application may apply the correlation factor as the likelihood that the current user will change the service. For example, if a first user exhibits similar behavior (evidenced by common actions or pattern of actions) as a second user, resulting in a relatively high correlation factor, and the second user terminated the service, then the first user has a relatively high likelihood of also terminating the service. In some embodiments, the media guidance application may combine the correlation factors of the plurality of users, either as a weighted or unweighted average, to compute the likelihood that the user will request to change the service. At step 922, the media guidance application may transmit (e.g., via control circuitry 304 (FIG. 3) through network 414 (FIG. 4)) the likelihood that the user will request to change the service to a service provider. The service provider may user this information to determine users at high-risk of terminating or downgrading the service, and may respond by sending advertisements or discount offers to the these users. At step 924, the media guidance application may determine (e.g., via control circuitry 304 (FIG. 3)) whether unselected users remain. If unselected users remain, then the media guidance application may select another user of the plurality of users at step 926 and loop back to step 914. If no unselected users remain, then the process 900 ends at step 928.

The above-described embodiments of the present disclosure are presented for purposes of illustration and not of limitation, and the present disclosure is limited only by the claims that follow. Furthermore, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and flowcharts or examples relating to one embodiment may be combined with any other embodiment in a suitable manner, done in different orders, or done in parallel. In addition, the systems and methods described herein may be performed in real time. It should also be noted that the systems and/or methods described above may be applied to, or used in accordance with, other systems and/or methods. 

1. A method for generating a model to determine a likelihood that a user will change a service, the method comprising: identifying a first service to which a plurality of users are subscribed; receiving information from a third-party data source for each of the plurality of users; identifying, based on the respective information from the third-party data source, a user action performed by each of the plurality of users in relation to the first service; identifying for at least one of the plurality of users, based on the respective information from the third-party data source, a request to change the first service; and generating a model to determine a likelihood that a user will change the first service based on the identified user action and the identified request to change the first service.
 2. The method of claim 1, wherein generating the model to determine the likelihood that the user will change the first service comprises calculating a probability that the identified user action is followed by the identified request to change the first service.
 3. The method of claim 1, wherein the user action performed by each of the plurality of users is a first user action, the method further comprising: identifying, based on the respective information from the third-party data source, a second user action performed by each of the plurality of users in relation to the first service; determining a pattern of action based on the first user action and the second user action; and wherein generating the model to determine the likelihood that the user will change the first service comprises calculating a probability that the determined pattern of action is followed by the identified request to change the first service.
 4. The method of claim 1, wherein the user action is one of a playback action, negative feedback for the first service, a negative comment made about the first service, or a lack of user action for a period of time.
 5. The method of claim 1, further comprising: receiving information from the third-party data source for the user; identifying, based on the information from the third-party data source for the user, a user action performed by the user in relation to the first service; comparing the user action performed by the user to the user action performed by each of the plurality of users; and calculating, based on the comparison, a correlation factor that indicates a similarity between the user and one of the plurality of users.
 6. The method of claim 5, further comprising determining, based on the correlation factor, a probability that the user will request to change the first service.
 7. The method of claim 5, further comprising transmitting the correlation factor to a service provider of the first service.
 8. The method of claim 5, further comprising transmitting to the user at least one of an advertisement for the first service or an offer for a discount for the first service.
 9. The method of claim 1, wherein the request to change the first service occurs within a user-defined period of time of the user action performed by the at least one of the plurality of users.
 10. The method of claim 1, wherein the third-party data source is a social media data source.
 11. A system for generating a model to determine a likelihood that a user will change a service, the system comprising: control circuitry configured to: identify a first service to which a plurality of users are subscribed; receive information from a third-party data source for each of the plurality of users; identify, based on the respective information from the third-party data source, a user action performed by each of the plurality of users in relation to the first service; identify for at least one of the plurality of users, based on the respective information from the third-party data source, a request to change the first service; and generate a model to determine a likelihood that a user will change the first service based on the identified user action and the identified request to change the first service.
 12. The system of claim 11, wherein the control circuitry is configured to generate the model to determine the likelihood that the user will change the first service by calculating a probability that the identified user action is followed by the identified request to change the first service.
 13. The system of claim 11, wherein the user action performed by each of the plurality of users is a first user action, the control circuitry further configured to: identify, based on the respective information from the third-party data source, a second user action performed by each of the plurality of users in relation to the first service; determine a pattern of action based on the first user action and the second user action; and wherein the control circuitry is configured to generate the model to determine the likelihood that the user will change the first service by calculating a probability that the determined pattern of action is followed by the identified request to change the first service.
 14. The system of claim 11, wherein the user action is one of a playback action, negative feedback for the first service, a negative comment made about the first service, or a lack of user action for a period of time.
 15. The system of claim 11, the control circuitry is further configured to: receive information from the third-party data source for the user; identify, based on the information from the third-party data source for the user, a user action performed by the user in relation to the first service; compare the user action performed by the user to the user action performed by each of the plurality of users; and calculate, based on the comparison, a correlation factor that indicates a similarity between the user and one of the plurality of users.
 16. The system of claim 15, the control circuitry is further configured to determine, based on the correlation factor, a probability that the user will request to change the first service.
 17. The system of claim 15, the control circuitry further configured to transmit the correlation factor to a service provider of the first service.
 18. The system of claim 15, the control circuitry further configured to transmit to the user at least one of an advertisement for the first service or an offer for a discount for the first service.
 19. The system of claim 11, wherein the request to change the first service occurs within a user-defined period of time of the user action performed by the at least one of the plurality of users.
 20. The system of claim 11, wherein the third-party data source is a social media data source. 21-50. (canceled) 